Abstract:

A "Hybrid Ship Track Detection System" is proposed for fast, accurate, and automatically processing multi-channel satellite cloud imagery, focusing on 3.7 and 0.63 micron to detect ship tracks. The configuration of the "Hybrid Ship Track Detection System" includes the following processing phases: (1) data acquisition and pre-processing, in order to reduce the noise and to enhance the figure-to-background contrast, (2) quick selection of ship track suspects, based upon the most prominent feature of ship track - high contrast in contiguous pixels, and (3) complete feature space determination and neural detection of tracks. The proposed R&D work is aimed on extending existing digital processing techniques, developing new ones, and introducing robust neural architectures for improving the speed and accuracy in the detection and classification of tracks. To test feasibility during Phase I, research will (1) develop, test, and automate the pre-processing and quick selection algorithms; (2) analyze the ship track suspects and to derive the additional relevant parameters and characteristic patterns, which subsequently are used for the classification task; (3) develop the neural network classifaction architectures; and (4) test and assess the performance of hybrid detection system using sample cloud images with typical ship track features, with special attention paid to success rate, false alarm rate, and robustness. This will lay the ground work for the developing final algorithms, with code adhering to modern programming standards, complete documentation, and final report detailing results of test cases during Phase II.